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About This Role
DESCRIPTION
Can a large language model read a regulatory change and draft the requirements and control tests that follow from it, accurately enough for a Compliance Officer to sign off? How do you train a breach-risk model when confirmed breaches are rare, arrive months after the event, and mean different things to Compliance, Assurance, and the regulator? What does a model have to prove before a Compliance team acts on its output?
We are looking for a Senior Applied Scientist to join the SPARC team within Risk and Compliance Solutions (RCS). SPARC's bold outcome is to accelerate the end-to-end regulatory change cycle by 10x, supercharging Compliance Officers as part of Amazon’s first AI-Native Second Line of Defense (2LoD). Our team builds the data, analytics, and modeling layer that RCS runs on, from regulatory reports and filings to the intelligence that tells Compliance Officers and our first line of defense (1LoD) partners where risk concentrates and which controls hold. You build the machine learning (ML) and generative artificial intelligence (GenAI) systems in that layer, take them into production, and own the evaluation science that lets Compliance rely on them.
The work spans 3 areas: risk models that score which open risks and control weaknesses convert to breaches; GenAI systems that remove manual effort from requirement translation, control testing, and regulatory reporting, with human approval gates at every stage; and the evaluation science behind every model and agent the team ships. Financial crime compliance is the first domain, and its constraints shape the science: threats change faster than control sets adapt, and every output faces internal audit and external examination. Once an approach proves out, you carry it into the next compliance domain across the RCS portfolio.
As a Senior Applied Scientist on SPARC, you own the science strategy for this layer, not just its individual models. You design and architect the agentic systems at the heart of it — objective-driven agents with human approval gates — and customize the technology to real compliance use cases. You define a multi-year science vision and roadmap for ML and GenAI in RCS, translate it into concrete plans for scientists and engineering partners, and anticipate where generative AI is heading so what you build capitalizes on compounding advances. You are the technical leader in AI/ML for the team and across partner teams, setting the standard for scientific rigor, raising the bar in design and model reviews, and mentoring scientists and engineers.
You own problems from formulation to production: framing the science question with Compliance and 1LoD partners, designing experiments and backtests, building and validating models, and shipping them into the pipelines, reports, and tools that RCS teams use. The stack includes Amazon SageMaker, Amazon Bedrock, Amazon Redshift, and AWS Lambda, and your partners include data scientists, data engineers, business intelligence engineers, and software development engineers across RCS.
The successful candidate treats an auditor's question as a design requirement, writes production-quality code alongside their models, and stays comfortable when the evaluation set does not exist until they build it.
Key job responsibilities
- Formulate ambiguous compliance problems as ML and GenAI problems with Compliance and 1LoD partners, and choose methods that fit the data, the risk, and the audit standard
- Develop risk models that learn from rare, delayed, and noisy labels to score which risks and control weaknesses convert to breaches, and calibrate them so a score carries a defensible meaning
- Build GenAI systems that remove manual effort from requirement translation, control testing, and regulatory reporting, with human approval gates at every stage
- Design the evaluation for every ML and GenAI system you ship: golden sets that compliance experts label, metrics suited to rare outcomes and generated text, and release criteria a system meets before Compliance relies on it
- Deploy and monitor models in production with software development engineers, including drift detection, performance tracking against realized outcomes, and retraining criteria
- Explain model decisions, quantify uncertainty, and document methodology so Compliance Officers, auditors, and examiners can trace how a system reached each output
- Create reusable science components, such as feature sets, scoring services, and evaluation harnesses, so an approach that works in financial crime compliance extends to the next domain without a rebuild
- Design and architect the pod's agentic systems and customize them to compliance use cases, working toward 10x acceleration of the end-to-end regulatory change cycle while maintaining or improving quality
- Set the technical direction for the pod's science: lead complex model and design reviews across teams, define evaluation methods and release standards others adopt, mentor data scientists and engineers on ML methods and experimental design, and publish or patent where the work and confidentiality allow
- Define a long-term science vision for RCS compliance AI driven from Compliance and 1LoD needs, translate it into actionable plans for science, engineering, and product partners, and influence where the science impacts the portfolio next
About the team
Risk and Compliance Solutions (RCS) serves as a crucial safeguard for Amazon's buyers, brands, and selling partners while ensuring business teams meet their regulatory obligations. RCS operates as the second line of defense under the Three Lines of Defense (3LoD) model, providing specialized oversight, monitoring, and expertise while setting policies and procedures that guide Amazon's compliance framework.
RCS's mission is to apply specialist industry insights and Amazon's unique approach to scale through technology, delighting customers through tailored compliance solutions. Payment regulatory compliance keeps the financial system closed to bad actors while widening access for hundreds of millions of good customers, and protects the licenses behind roughly $650B in annual activity across 18 entities and 45 regulated products in 6 regions.BASIC QUALIFICATIONS
- PhD, or Master's degree and 5+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- 3+ years of building machine learning models for business application experience
PREFERRED QUALIFICATIONS
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with neural deep learning methods and machine learning
- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, CA, San Diego - 167,100.00 - 226,100.00 USD annually
Salary Context
This $167K-$226K range is below the median for Research Scientist roles in our dataset (median: $196K across 100 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 11,520 AI roles we're tracking, Research Scientist positions make up 2% of the market. At Amazon.com, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $223,400 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($196K) sits 12% below the category median. Disclosed range: $167K to $226K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Amazon.com AI Hiring
Amazon.com has 79 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span New York, NY, US, Seattle, WA, US, San Diego, CA, US. Compensation range: $148K - $327K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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